# 绘制alpha多样性状图+误差棒+统计分组 Alpha barplot + error bar + test
#
# The function named 'alpha_barplot'
# which draw barplot + error bar + test alphabet with alpha and metadata, and reture a ggplot2 object
#
# You can learn more about package at:
#
#   https://github.com/microbiota/amplicon

#' @title Plotting alpha diversity barplot for each group with anova statistics
#' @description Input alpha index and metadata, and manual set alpha index and metadata column names.
#' ANOVA or agricolae::LSD.test calculate p-value, and dplyr summary each group max for p-value groups position.
#' ggplot2 show barplot, error bar and stat groups.
#' @param alpha_div alpha diversity matrix, typical output of usearch -alpha_div or vegan estimateR/diversity(),
#' rowname is sampleID, colname is index of alpha diversity;
#' @param index index types of alpha diversity;
#' @param metadata matrix or dataframe, including sampleID and groupID;
#' @param groupID column name for groupID.
#' @details
#' By default, input alpha diversity and metatdata, and output plot
#' The available diversity indices include the following:
#' \itemize{
#' \item{most used indices: chao1, richness, shannon_e}
#' \item{other used indices: berger_parker, buzas_gibson, dominance, equitability, jost, jost1, reads, robbins, simpson, shannon_2, shannon_10}
#' }
#' @return ggplot2 object.
#' @author Contact: Tao Wen \email{2018203048@@njau.edu.cn}, Yong-Xin Liu \email{yxliu@@genetics.ac.cn}
#' @references
#'
#' Jingying Zhang, Yong-Xin Liu, Na Zhang, Bin Hu, Tao Jin, Haoran Xu, Yuan Qin, Pengxu Yan, Xiaoning Zhang, Xiaoxuan Guo, Jing Hui, Shouyun Cao, Xin Wang, Chao Wang, Hui Wang, Baoyuan Qu, Guangyi Fan, Lixing Yuan, Ruben Garrido-Oter, Chengcai Chu & Yang Bai.
#' NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice.
#' Nature Biotechnology, 2019(37), 6:676-684, DOI: \url{https://doi.org/10.1038/s41587-019-0104-4}
#'
#' @seealso alpha_rare alpha_boxplot
#' @examples
#' # Set four parameters: alpha_div, metadata, index and groupID
#' alpha_barplot(alpha_div, metadata, "richness", "genotype")
#' # Set two parameters: alpha_div, metadata, and index and groupID as using chao1 and site
#' alpha_barplot(alpha_div, metadata, "chao1", "site")
#' # Set four parameters: alpha_div, metadata, index and groupID with full parameters
#' alpha_barplot(alpha_div = alpha_div, metadata = metadata, index = "richness", groupID = "genotype")
#' @export

#代码测试
# load("../data/alpha_div.rda")
# load("../data/metadata.rda")
# p = alpha_barplot(alpha_div, metadata, index = "richness", groupID = "genotype")
# p
# index = "richness"
# groupID = "genotype"



alpha_barplot <- function(alpha_div, metadata, index = "richness", groupID = "genotype") {

  # 依赖关系检测与安装
  p_list = c("ggplot2", "dplyr", "multcompView") # "agricolae"
  for(p in p_list){
    if (!requireNamespace(p)){
    install.packages(p)}
    suppressPackageStartupMessages(library(p, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE))
  }

  # 测试默认参数
  # library(amplicon)
  # index = "richness"
  # groupID = "genotype"
  # metadata = subset(metadata, genotype %in% c("KO","OE"))

  # 交叉筛选
  idx = rownames(metadata) %in% rownames(alpha_div)
  metadata = metadata[idx,,drop=F]
  alpha_div = alpha_div[rownames(metadata),]

  # 提取样品组信息,默认为group可指定
  sampFile = as.data.frame(metadata[, groupID],row.names = row.names(metadata))
  # colnames(sampFile)[1] = "group"

  # 合并alpha_div和metadata
  df = cbind(alpha_div[rownames(sampFile),index], sampFile)
  colnames(df) = c(index,"group")
  # df


  # 统计各种显著性
  model = aov(df[[index]] ~ group, data=df)
  # 计算Tukey显著性差异检验
  Tukey_HSD = TukeyHSD(model, ordered = TRUE, conf.level = 0.95)
  # 提取比较结果
  Tukey_HSD_table = as.data.frame(Tukey_HSD$group)

  # 保存统计结果
  stat_tab="alpha_boxplot_TukeyHSD.txt"
  # 保存一个制表符，解决存在行名时，列名无法对齐的问题
  write.table(paste(date(), "\nGroup\t", groupID, "\n\t", sep=""), file=paste(stat_tab, sep=""),append = T, quote = F, eol = "", row.names = F, col.names = F)
  # 保存统计结果，有waring正常
  suppressWarnings(write.table(Tukey_HSD_table, file=paste(stat_tab, sep=""), append = T, quote = F, sep="\t", eol = "\n", na = "NA", dec = ".", row.names = T, col.names = T))
  print(paste0("Statistic table is in ", stat_tab))

  # 函数：将Tukey检验结果P值转换为显著字母分组
  # 输入文件为图基检验结果和分组
  generate_label_df = function(TUKEY, variable){
    library(multcompView)
    # 转换P值为字母分组
    ## 提取图基检验中分组子表的第4列P adjust值
    Tukey.levels = TUKEY[[variable]][,4]
    ## multcompLetters函数将两两p值转换为字母，data.frame并生成列名为Letters的数据框
    Tukey.labels = data.frame(multcompLetters(Tukey.levels)['Letters'])

    # 按分组名字母顺序
    ## 提取字母分组行名为group组名
    Tukey.labels$group = rownames(Tukey.labels)
    # 按组名的字母顺序排列，默认的Levels
    Tukey.labels=Tukey.labels[order(Tukey.labels$group), ]
    return(Tukey.labels)
  }

  # 当只有两组时，用LSD标注字母
  if (length(unique(df$group)) == 2){
    # LSD检验，添加差异组字母
    library(agricolae)
    out = LSD.test(model, "group", p.adj="none")
    stat = out$groups
    # 分组结果添入Index
    df$stat=stat[as.character(df$group),]$groups
  # 当大于两组时，用multcompView标注字母
  }else{
    # library(multcompView)
    LABELS = generate_label_df(Tukey_HSD , "group")
    df$stat=LABELS[as.character(df$group),]$Letters
  }

  # 设置分组位置为各组y最大值+高的5%
  max=max(df[,c(index)])
  min=min(df[,index])
  x = df[,c("group",index)]
  y = x %>% group_by(group) %>% summarise_(Max=paste('max(',index,')',sep=""))
  y=as.data.frame(y)
  rownames(y)=y$group
  df$y=y[as.character(df$group),]$Max + (max-min)*0.05
  # head(df)

  #求取均值和方差
  wen1 = as.data.frame(tapply(as.vector(as.matrix(df[1])), df$group, mean, na.rm=TRUE))
  wen2 = as.data.frame(tapply(as.vector(as.matrix(df[1])), df$group, sd, na.rm=TRUE))
  went = cbind(wen1, wen2)
  colnames(went) = c("mean","SD")
  # went

  #去除重复，用来提取显著性字母
  aa = distinct(df, group, .keep_all = TRUE)
  #将显著性字母添加到表格中
  went$label = aa$stat[match(row.names(went),aa$group)]
  went$Groups = row.names(went)
  #显著性标记由于坐标轴长度不够会遮盖字母，所以我这里人工设定一个y轴坐标大小，是最大值的1.1倍
  a = max(went$mean + went$SD)*1.1

  # 绘图 plotting
  # 按X轴分组着色，均值Y轴；
  p = ggplot(went, aes(x = Groups, y = mean, colour= Groups)) +
    geom_bar(aes(colour= Groups, fill = Groups), stat = "identity", width = 0.4, position = "dodge") +
    scale_y_continuous(expand = c(0,0),limits = c(0,a)) +
    geom_errorbar(aes(ymin = mean-SD, ymax=mean+SD), colour="black", width=0.1, size = 1) +
    geom_text(aes(label = label,y = mean+SD, x = Groups, vjust = -0.3), color = "black") +
    theme_classic() +
    theme(text=element_text(family="sans", size=7))
  p
}
